Free Practice Questions for Amazon MLS-C01 Exam

A trucking company is collecting live image data from its fleet of trucks across the globe. The data is growing rapidly and approximately 100 GB of new data is generated every day. The company wants to explore machine learning uses cases while ensuring the data is only accessible to specific IAM users.Which storage option provides the most processing flexibility and will allow access control with IAM?

• Use a database, such as Amazon DynamoDB, to store the images, and set the IAM policies to restrict access to only the desired IAM users.
• Use an Amazon S3-backed data lake to store the raw images, and set up the permissions using bucket policies.
• Setup up Amazon EMR with Hadoop Distributed File System (HDFS) to store the files, and restrict access to the EMR instances using IAM policies.
• Configure Amazon EFS with IAM policies to make the data available to Amazon EC2 instances owned by the IAM users.

A Machine Learning Specialist is designing a system for improving sales for a company. The objective is to use the large amount of information the company has on users' behavior and product preferences to predict which products users would like based on the users' similarity to other users.What should the Specialist do to meet this objective?

• Build a content-based filtering recommendation engine with Apache Spark ML on Amazon EMR.
• Build a collaborative filtering recommendation engine with Apache Spark ML on Amazon EMR.
• Build a model-based filtering recommendation engine with Apache Spark ML on Amazon EMR.
• Build a combinative filtering recommendation engine with Apache Spark ML on Amazon EMR.

Acybersecurity company is collecting on-premises server logs, mobile app logs, and loT sensor data. The company backs up the ingested data in an Amazon S3 bucket and sends the ingested data to Amazon OpenSearch Service for further analysis. Currently, the company has a custom ingestion pipeline that is running on Amazon EC2 instances. The company needs to implement a new serverless ingestion pipeline that can automatically scale to handle sudden changes in the data flow.Which solution will meet these requirements MOST cost-effectively?

• Create two Amazon Data Firehose delivery streams to send data to the S3 bucket and OpenSearch Service. Configure the data sources to send data to the delivery streams.
• Create one Amazon Kinesis data stream. Create two Amazon Data Firehose delivery streams to send data to the S3 bucket and OpenSearch Service. Connect the delivery streams to the data stream. Configure the data sources to send data to the data stream.
• Create one Amazon Data Firehose delivery stream to send data to OpenSearch Service. Configure the delivery stream to back up the raw data to the S3 bucket. Configure the data sources to send data to the delivery stream.
• Create one Amazon Kinesis data stream. Create one Amazon Data Firehose delivery stream to send data to OpenSearch Service. Configure the delivery stream to back up the data to the S3 bucket. Connect the delivery stream to the data stream. Configure the data sources to send data to the data stream.

A bank's Machine Learning team is developing an approach for credit card fraud detection The company has a large dataset of historical data labeled as fraudulent The goal is to build a model to take the information from new transactions and predict whether each transaction is fraudulent or notWhich built-in Amazon SageMaker machine learning algorithm should be used for modeling this problem?

• Seq2seq
• XGBoost
• K-means
• Random Cut Forest (RCF)

A machine learning specialist is developing a proof of concept for government users whose primary concern is security. The specialist is using Amazon SageMaker to train a convolutional neural network (CNN) model for a photo classifier application. The specialist wants to protect the data so that it cannot be accessed and transferred to a remote host by malicious code accidentally installed on the training container.Which action will provide the MOST secure protection?

• Remove Amazon S3 access permissions from the SageMaker execution role.
• Encrypt the weights of the CNN model.
• Encrypt the training and validation dataset.
• Enable network isolation for training jobs.

A Data Science team is designing a dataset repository where it will store a large amount of training data commonly used in its machine learning models. As Data Scientists may create an arbitrary number of new datasets every day the solution has to scale automatically and be cost-effective. Also, it must be possible to explore the data using SQL.Which storage scheme is MOST adapted to this scenario?

• Store datasets as files in Amazon S3.
• Store datasets as files in an Amazon EBS volume attached to an Amazon EC2 instance.
• Store datasets as tables in a multi-node Amazon Redshift cluster.
• Store datasets as global tables in Amazon DynamoDB.

A company that runs an online library is implementing a chatbot using Amazon Lex to provide book recommendations based on category. This intent is fulfilled by an AWS Lambda function that queries an Amazon DynamoDB table for a list of book titles, given a particular category. For testing, there are only three categories implemented as the custom slot types: 'comedy,' 'adventure,'' and 'documentary.''A machine learning (ML) specialist notices that sometimes the request cannot be fulfilled because Amazon Lex cannot understand the category spoken by users with utterances such as 'funny,' 'fun,' and 'humor.' The ML specialist needs to fix the problem without changing the Lambda code or data in DynamoDB.How should the ML specialist fix the problem?

• Add the unrecognized words in the enumeration values list as new values in the slot type.
• Create a new custom slot type, add the unrecognized words to this slot type as enumeration values, and use this slot type for the slot.
• Use the AMAZON.SearchQuery built-in slot types for custom searches in the database.
• Add the unrecognized words as synonyms in the custom slot type.

A company is converting a large number of unstructured paper receipts into images. The company wants to create a model based on natural language processing (NLP) to find relevant entities such as date, location, and notes, as well as some custom entities such as receipt numbers.The company is using optical character recognition (OCR) to extract text for data labeling. However, documents are in different structures and formats, and the company is facing challenges with setting up the manual workflows for each document type. Additionally, the company trained a named entity recognition (NER) model for custom entity detection using a small sample size. This model has a very low confidence score and will require retraining with a large dataset.Which solution for text extraction and entity detection will require the LEAST amount of effort?

• Extract text from receipt images by using Amazon Textract. Use the Amazon SageMaker BlazingText algorithm to train on the text for entities and custom entities.
• Extract text from receipt images by using a deep learning OCR model from the AWS Marketplace. Use the NER deep learning model to extract entities.
• Extract text from receipt images by using Amazon Textract. Use Amazon Comprehend for entity detection, and use Amazon Comprehend custom entity recognition for custom entity detection.
• Extract text from receipt images by using a deep learning OCR model from the AWS Marketplace. Use Amazon Comprehend for entity detection, and use Amazon Comprehend custom entity recognition for custom entity detection.

A company is setting up a mechanism for data scientists and engineers from different departments to access an Amazon SageMaker Studio domain. Each department has a unique SageMaker Studio domain.The company wants to build a central proxy application that data scientists and engineers can log in to by using their corporate credentials. The proxy application will authenticate users by using the company's existing Identity provider (IdP). The application will then route users to the appropriate SageMaker Studio domain.The company plans to maintain a table in Amazon DynamoDB that contains SageMaker domains for each department.How should the company meet these requirements?

• Use the SageMaker CreatePresignedDomainUrl API to generate a presigned URL for each domain according to the DynamoDB table. Pass the presigned URL to the proxy application.
• Use the SageMaker CreateHuman TaskUi API to generate a UI URL. Pass the URL to the proxy application.
• Use the Amazon SageMaker ListHumanTaskUis API to list all UI URLs. Pass the appropriate URL to the DynamoDB table so that the proxy application can use the URL.
• Use the SageMaker CreatePresignedNotebookInstanceUrl API to generate a presigned URL. Pass the presigned URL to the proxy application.

A retail chain has been ingesting purchasing records from its network of 20,000 stores to Amazon S3 using Amazon Kinesis Data Firehose To support training an improved machine learning model, training records will require new but simple transformations, and some attributes will be combined The model needs lo be retrained dailyGiven the large number of stores and the legacy data ingestion, which change will require the LEAST amount of development effort?

• Require that the stores to switch to capturing their data locally on AWS Storage Gateway for loading into Amazon S3 then use AWS Glue to do the transformation
• Deploy an Amazon EMR cluster running Apache Spark with the transformation logic, and have the cluster run each day on the accumulating records in Amazon S3, outputting new/transformed records to Amazon S3
• Spin up a fleet of Amazon EC2 instances with the transformation logic, have them transform the data records accumulating on Amazon S3, and output the transformed records to Amazon S3.
• Insert an Amazon Kinesis Data Analytics stream downstream of the Kinesis Data Firehouse stream that transforms raw record attributes into simple transformed values using SQL.